metadata
library_name: transformers
license: gemma
language:
- en
- hi
- ta
- te
- kn
- mr
- ml
- bn
- gu
- or
tags:
- gemma3
- intent-classification
- multilingual
base_model: google/gemma-3-270m
pipeline_tag: text-classification
Multilingual_Intent_Classifier_checkpoint_v1
PyTorch checkpoint behind blue-machines/Multilingual_Intent_classifier_v1. Use this repo to keep fine-tuning. The Hub ONNX file is the INT8 deploy build of these weights, with the linear intent head left in FP32.
Model
8-layer Gemma 3 text stack, hidden size 320, about 100 million parameters. A mean pool over the encoder states feeds a linear intent head.
Intents
provide_info, affirm, deny, correction, question, clarify_request, unclear
Languages
Hindi, Tamil, Telugu, Kannada, Marathi, Malayalam, Bengali, Gujarati, Odia, and English. Utterances may be in the native script, romanized, or code-mixed.
Load
AutoModel cannot construct this student. Load Gemma3Intent8LStudent from the training code, then the weights in this repo:
from pathlib import Path
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
from transformers import AutoConfig, AutoTokenizer
from train_intent_multilang_indic_gemma270m_8L_distill import Gemma3Intent8LStudent
repo = "blue-machines/Multilingual_Intent_Classifier_checkpoint_v1"
local = Path(snapshot_download(repo))
config = AutoConfig.from_pretrained(local)
tokenizer = AutoTokenizer.from_pretrained(local)
model = Gemma3Intent8LStudent(config)
missing, unexpected = model.load_state_dict(
load_file(local / "model.safetensors"), strict=False
)